


"""simple docstring"""


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 	snake_case		(  UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    Tuple      ,					UpperCamelCase__							:    List[str]     ) ->   Optional[Any]:
      return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]




def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    Tuple      ,					UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Optional[int]="attention"     ) ->   str:
      lowerCamelCase       :       int   =						np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]     )
      lowerCamelCase       :       Union[str, Any]   =						k_tmp.reshape(k_tmp.shape[0]      ,					k_tmp.shape[1] * k_tmp.shape[2]     )
      lowerCamelCase       :       Union[str, Any]   =						np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]     )
      lowerCamelCase       :       Union[str, Any]   =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,					o_tmp.shape[2]     )
      lowerCamelCase       :       Optional[int]   =						np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]     )
      lowerCamelCase       :       int   =						q_tmp.reshape(q_tmp.shape[0]      ,					q_tmp.shape[1] * q_tmp.shape[2]     )
      lowerCamelCase       :       int   =						np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]     )
      lowerCamelCase       :       Tuple   =						v_tmp.reshape(v_tmp.shape[0]      ,					v_tmp.shape[1] * v_tmp.shape[2]     )
      return k, o, q, v




def 	snake_case		(  UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Tuple      ,					UpperCamelCase__							:    str      ,					UpperCamelCase__							:    Tuple=False     ) ->   Optional[int]:
      if split_mlp_wi:
            lowerCamelCase       :       Optional[int]   =						params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
            lowerCamelCase       :       Any   =						params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
            lowerCamelCase       :       Optional[Any]   =						(wi_a, wi_a)
      else:
            lowerCamelCase       :       Optional[Any]   =						params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

      lowerCamelCase       :       Optional[int]   =						params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
      return wi, wo




def 	snake_case		(  UpperCamelCase__							:    List[Any]      ,					UpperCamelCase__							:    int      ,					UpperCamelCase__							:    Optional[Any]      ,					UpperCamelCase__							:    Optional[int]     ) ->   int:
      return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]




def 	snake_case		(  UpperCamelCase__							:    dict      ,					*, UpperCamelCase__							:    int      ,					UpperCamelCase__							:    bool      ,					UpperCamelCase__							:    bool = False     ) ->   str:
      lowerCamelCase       :       List[str]   =						traverse_util.flatten_dict(variables["""target"""]     )
      lowerCamelCase       :       str   =						{"""/""".join(_lowerCamelCase     ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      lowerCamelCase       :       str   =						"""encoder/encoder/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""      ,					_lowerCamelCase     )

      lowerCamelCase       :       Union[str, Any]   =						collections.OrderedDict()

      # Shared embeddings.
      lowerCamelCase       :       Union[str, Any]   =						old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(_lowerCamelCase     ):
            # Block i, layer 0 (Self Attention).
            lowerCamelCase       :       Any   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					"""pre_attention_layer_norm"""     )
            lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       Union[str, Any]   =						tax_attention_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					"""attention"""     )
            lowerCamelCase       :       Tuple   =						layer_norm
            lowerCamelCase       :       List[Any]   =						k.T
            lowerCamelCase       :       int   =						o.T
            lowerCamelCase       :       Tuple   =						q.T
            lowerCamelCase       :       int   =						v.T

            # Block i, layer 1 (MLP).
            lowerCamelCase       :       int   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					"""pre_mlp_layer_norm"""     )
            lowerCamelCase  ,      lowerCamelCase       :       Tuple   =						tax_mlp_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					_lowerCamelCase     )
            lowerCamelCase       :       List[str]   =						layer_norm
            if split_mlp_wi:
                  lowerCamelCase       :       List[str]   =						wi[0].T
                  lowerCamelCase       :       Any   =						wi[1].T
            else:
                  lowerCamelCase       :       List[Any]   =						wi.T
            lowerCamelCase       :       Any   =						wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  lowerCamelCase       :       Dict   =						tax_relpos_bias_lookup(
                      _lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""     ).T

      lowerCamelCase       :       List[Any]   =						old["""encoder/encoder_norm/scale"""]

      if not scalable_attention:
            lowerCamelCase       :       Optional[int]   =						tax_relpos_bias_lookup(
                _lowerCamelCase      ,					0      ,					"""encoder"""     ).T
            lowerCamelCase       :       Optional[int]   =						tax_relpos_bias_lookup(
                _lowerCamelCase      ,					0      ,					"""decoder"""     ).T

      if not is_encoder_only:
            # Decoder.
            for i in range(_lowerCamelCase     ):
                  # Block i, layer 0 (Self Attention).
                  lowerCamelCase       :       int   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""pre_self_attention_layer_norm"""     )
                  lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       Tuple   =						tax_attention_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""self_attention"""     )
                  lowerCamelCase       :       Optional[int]   =						layer_norm
                  lowerCamelCase       :       Dict   =						k.T
                  lowerCamelCase       :       int   =						o.T
                  lowerCamelCase       :       Union[str, Any]   =						q.T
                  lowerCamelCase       :       Union[str, Any]   =						v.T

                  # Block i, layer 1 (Cross Attention).
                  lowerCamelCase       :       Union[str, Any]   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""pre_cross_attention_layer_norm"""     )
                  lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       Any   =						tax_attention_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""encoder_decoder_attention"""     )
                  lowerCamelCase       :       Tuple   =						layer_norm
                  lowerCamelCase       :       Optional[int]   =						k.T
                  lowerCamelCase       :       int   =						o.T
                  lowerCamelCase       :       Optional[int]   =						q.T
                  lowerCamelCase       :       Union[str, Any]   =						v.T

                  # Block i, layer 2 (MLP).
                  lowerCamelCase       :       Optional[int]   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""pre_mlp_layer_norm"""     )
                  lowerCamelCase  ,      lowerCamelCase       :       Union[str, Any]   =						tax_mlp_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					_lowerCamelCase     )
                  lowerCamelCase       :       Optional[int]   =						layer_norm
                  if split_mlp_wi:
                        lowerCamelCase       :       Tuple   =						wi[0].T
                        lowerCamelCase       :       Any   =						wi[1].T
                  else:
                        lowerCamelCase       :       int   =						wi.T
                  lowerCamelCase       :       Optional[Any]   =						wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        lowerCamelCase       :       List[Any]   =						tax_relpos_bias_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""     ).T

            lowerCamelCase       :       int   =						old["""decoder/decoder_norm/scale"""]

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  lowerCamelCase       :       List[str]   =						old["""decoder/logits_dense/kernel"""].T

      return new




def 	snake_case		(  UpperCamelCase__							:    Tuple      ,					UpperCamelCase__							:    bool     ) ->   Tuple:
      lowerCamelCase       :       Any   =						collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            lowerCamelCase       :       Union[str, Any]   =						state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowerCamelCase       :       Union[str, Any]   =						state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("""Using shared word embeddings as lm_head."""     )
                  lowerCamelCase       :       List[str]   =						state_dict["""shared.weight"""]

      return state_dict




def 	snake_case		(  UpperCamelCase__							:    Optional[Any]      ,					UpperCamelCase__							:    int      ,					UpperCamelCase__							:    Any      ,					UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Optional[Any]     ) ->   Optional[Any]:
      lowerCamelCase       :       Union[str, Any]   =						checkpoints.load_tax_checkpoint(_lowerCamelCase     )
      lowerCamelCase       :       List[str]   =						convert_tax_to_pytorch(
          _lowerCamelCase      ,					num_layers=config.num_layers      ,					is_encoder_only=_lowerCamelCase      ,					scalable_attention=_lowerCamelCase     )
      lowerCamelCase       :       str   =						make_state_dict(_lowerCamelCase      ,					_lowerCamelCase     )
      model.load_state_dict(_lowerCamelCase      ,					strict=_lowerCamelCase     )




def 	snake_case		(  UpperCamelCase__							:    Tuple      ,					UpperCamelCase__							:    Optional[Any]      ,					UpperCamelCase__							:    Optional[Any]      ,					UpperCamelCase__							:    bool = False      ,					UpperCamelCase__							:    bool = False      ,					) ->   Optional[Any]:
      lowerCamelCase       :       Tuple   =						MTaConfig.from_json_file(_lowerCamelCase     )
      print(F'Building PyTorch model from configuration: {config}'     )
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            lowerCamelCase       :       str   =						UMTaEncoderModel(_lowerCamelCase     )
      else:
            lowerCamelCase       :       Dict   =						UMTaForConditionalGeneration(_lowerCamelCase     )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase     )

      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'     )
      model.save_pretrained(_lowerCamelCase     )

      # Verify that we can load the checkpoint.
      model.from_pretrained(_lowerCamelCase     )
      print("""Done"""     )


if __name__ == "__main__":
    __lowerCamelCase   :str						     =		argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
    )
    parser.add_argument(
        '--scalable_attention',
        action='store_true',
        help='Whether the model uses scaled attention (umt5 model)',
        default=False,
    )
    __lowerCamelCase   :Tuple						     =		parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )












"""simple docstring"""





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						complex						,							_lowerCamelCase			:						str = "x"						,							_lowerCamelCase			:						float = 10**-10						,							_lowerCamelCase			:						int = 1						,							) ->							complex:
  lowerCamelCase_       	=		symbols(_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							diff(_lowerCamelCase						,							_lowerCamelCase					)					)

  lowerCamelCase_       	=		starting_point

  while True:
    if diff_function(_lowerCamelCase					) != 0:
      lowerCamelCase_       	=		prev_guess - multiplicity * func(_lowerCamelCase					) / diff_function(
          _lowerCamelCase					)
    else:
      raise ZeroDivisionError('Could not find root'					) from None

    # Precision is checked by comparing the difference of consecutive guesses
    if abs(next_guess - prev_guess					) < precision:
      return next_guess

    lowerCamelCase_       	=		next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}''')

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      F'''{newton_raphson('exp(x) - 1', 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')




